A fundamental component of ISR (Information, Surveillance, Reconnaissance) technologies pertains to all levels of data fusion and extrapolation. The greater need, however, is for methods that can parse natural language queries, map them to their semantic normalization, and retrieve information associatively tagged with the normalization in a seamless heterogeneous architecture. Retrieved information can be passive in the sense that it is limited to the data level or active in the sense that it may be a method for computing the desired information. The scientific goal is to make literal and latent information alike, which may be imbued in a semi-structured database, available for reuse and subsequent integration. While this is a computationally intensive process, it has the advantage of being maximally amenable to execution on fine-grained processors.
Unlike the case for neural network applications, a consequence of semantic retrieval is that resulting knowledge can be explained to the user by way of metaphor. Moreover, prior systems such as that described in U.S. Pat. No. 7,047,226 to Dr. Stuart H. Rubin, entitled SYSTEM AND METHOD FOR KNOWLEDGE AMPLIFICATION EMPLOYING STRUCTURED EXPERT RANDOMIZATION, the teachings and disclosure of which are hereby incorporated in their entireties by reference thereto, can generate analogous features from a feature set. This advancement over first-, second-, and even third-generation expert systems automatically expands the rule base without the concomitant data input burden associated with error correction needed to optimize expert system performance. The Rubin Knowledge Amplifier with Structured Expert Randomization (KASER) expert system described in the Rubin patent includes learning means for acquiring a rule system that functions as a larger virtual rule system with reduced error probability. Given that this semantic retrieval methodology can automatically learn to extract relevant phrases (i.e., features) and their sequence from a supplied query, the system will converge on ever-better sets of features and heterogeneous rules expressed in terms of those features for purposes of fusion and prediction.
The product of Dr. Rubin's later work on a semantic normalizer can be easily trained by a bilingual and otherwise ordinary user to translate natural languages (e.g., to backend commercial off-the-shelf (COTS) Arabic to English translators). This project also resulted in a novel learning algorithm for message summarization for use by various naval reporting agencies. However, it is clear that potential transitional customers for the semantic normalizer wanted a product that they did not have to train. Further, application domains such as battle management, logistics, signal analysis, targeting and tracking, counter-insurgency, as well as the development of intelligent auto pilots for Unmanned Aerial Vehicle-Smart Warfighting Array of Reconfigurable Modules (UAV (swarms)), among others, currently are also forced to rely on various combination of conventional methodologies (e.g., case-based reasoning, expert systems, genetic algorithms, neural networks, support-vector machines, etc.).
In view of the foregoing, it is clear that there is a need in the art for a hardwired natural language interface for a relational database structured query language (SQL) and a data mining capability that is capable of communication and learning, without requiring a human to train the system. The invention provides such a knowledge-based decision support system that allows for communication and learning to occur, using natural language. These and other advantages of the invention, as well as additional inventive features, will be apparent from the description of the invention provided herein.